Data-Driven Inverse Problem for Optimizing the Induction Hardening Process of C45 Spur-Gear

Inverse problems can be challenging and interesting to study in the context of metallurgical processes. This work aims carry out a method for inverse modeling simultaneous double-frequency induction hardening process. In this investigation, experimental measured hardness profiles were considered as input data, while output data process parameters. For purpose experiments carried on C45 steel spur-gear. The is based machine learning algorithms treatment dealing with approach issues. addition modeling, forward problem-based verification completes study. It was found that according promising results suitable applicable problem modeling.